I build systems that learn from data.

class ParallelScraper:
    def __init__(self, max_workers=5):
        self.executor = ThreadPoolExecutor(
            max_workers=max_workers
        )
        self.driver_pool = queue.Queue()

    def scrape_company(self, company_url):
        driver = self.driver_pool.get()
        try:
            driver.get(company_url)
            return extracted_data
        finally:
            self.driver_pool.put(driver)

def extract_successful_patterns(self, winning_proposals):
    """Identify patterns in successful grants"""
    patterns = {
        'structure': self.analyze_structure(winning_proposals),
        'keywords': self.extract_keywords(winning_proposals),
        'sentiment': self.analyze_sentiment(winning_proposals),
        'readability': self.calculate_readability(winning_proposals)
    }
    return patterns

def calculate_winner(self, test_id):
    """Statistical significance testing"""
    data = self.results[test_id]
    chi2, p_value, dof, expected = chi2_contingency(data)

    if p_value < 0.05:
        return self.identify_winner(data)
    return None  # No significant winner yet

def calculate_mastery_level(self, responses):
    """Skill mastery via Item Response Theory"""
    # Bayesian estimation of ability parameter
    theta = self.estimate_ability(responses)
    # Adaptive threshold based on learning curve
    threshold = self.get_adaptive_threshold(theta)
    return {
        'mastery': theta >= threshold,
        'score': float(theta),
        'gaps': self.detect_knowledge_gaps(theta)
    }